The BAD_ annotations were actually created by clicking-and-dragging the mouse in the annotation mode of the interactive MNE plot window. The data of the raw object was explored over the entire recording time and across all channels. For example, the complete beginning of the recording was always annotated as BAD_, because the electrodes need a certain time to get to equilibrium. Artefacts in EEG come from blinks, eye movements and muscle contractions. They have to be removed to increase the certainty that the results obtained later are indeed due to brain activity. The EOG signals gave an indication of where the subject blinked or moved his eyes. Corresponding time spans were annotated. It was also considered to which point in the experiment the examined data belonged. Cleaning was less restrictive at the times when the stimuli were presented in order not to exclude relevant brain activity from further processing.
In addition to cleaning in time by annotating the bad time segments, cleaning in space could also have been done via the interactive MNE plot window. A source in the brain spreads to many electrodes, there is a lot of redundancy and a high correlation between the channels. In some recordings it happens that an electrode is not correctly placed on the head of the subject, that it moves or is just broken. Visual inspection of the raw data of all three subjects did not reveal any noisy electrode. No noisy data was seen in any of the channels. Therefore, it was considered sufficient to limit cleaning via 101_annotate_raw.ipynb to the bad time segments. As a result, the files manual-badChannels.tsv are empty.
On the other hand ICs 0 and 11 were identified as eye components. The source of IC 0 is assumed to be blinks of the subject, IC 11 refers to horizontal eye movements of the subject. For both components, the scalp topography visualizes that the components have a strong effect on the electrodes placed close to the eyes. The plot thus indicates an origin of the source near the eyes. In the scalp topography of IC 11, maxima with opposite polarity occur at the front left and right. This is typical for horizontal eye movements of the subject. In the PSD, both ICs lack the peak at 10 Hz that is characteristic of a brain component. Instead, blink artefacts appear in a peak at the low frequency end of the spectrum. Especially IC 0 shows clear evidence of blinks. These show up in the image segment as relatively short stripe. They occur from time to time, but not evenly distributed and not in every segment. Another hint that the components are to be classified as artifacts is the plot of their variance. Most segments have a low variance. However, viewed over time, individual segments repeatedly have a very high variance. This indicates noise in the data.
After inspecting the IC property plots, the two ICs 0 and 11 were listed as bad components in the code of label_badComponents.py. Muscle components could not be detected in the ICA decomposition of Subject 002. Since their source is not within the brain, they would appear very flat on the scalp topography, i.e. relatively concentrated in small regions. In addition, increased high frequency activity is characteristic of muscle components. Neither the scalp topography plots nor the PSDs of the ICs of Subject 002 clearly indicate a muscle component. Therefore no further IC was marked for exclusion. Overall, the marking of bad components was based on the idea of marking as few as possible and only clearly artefactual ICs for exclusion. Otherwise, actual brain activity could be lost through cleaning.
The overlay plot was used to assess how well the cleaning worked by excluding the artifactual ICs. It shows the raw data before and after the ICA artifact rejection. In addition, a cross-channel average is shown. Dipolar sources are cancelled out by the averaging, whereas peaks are an indication of artefacts. The overlay plot of Subject 002 supports the assumption that artefacts were excluded from the signal with ICs 0 and 11. While clear peaks are visible in the signal before cleaning, they disappeared after cleaning.
| Method | infomax |
|---|---|
| Fit | 500 iterations on raw data (360448 samples) |
| ICA components | 28 |
| Explained variance | 100.0 % |
| Available PCA components | 30 |
| Channel types | eeg |
| ICA components marked for exclusion | ICA000 ICA011 |
| Method | infomax |
|---|---|
| Fit | 500 iterations on raw data (45056 samples) |
| ICA components | 28 |
| Explained variance | 100.0 % |
| Available PCA components | 30 |
| Channel types | eeg |
| ICA components marked for exclusion | ICA000 ICA002 ICA021 |
| Method | infomax |
|---|---|
| Fit | 500 iterations on raw data (468992 samples) |
| ICA components | 28 |
| Explained variance | 100.0 % |
| Available PCA components | 30 |
| Channel types | eeg |
| ICA components marked for exclusion | ICA000 ICA008 ICA010 ICA018 |